Upload 4 files

app.py

@@ -0,0 +1,107 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.layers import Dense, BatchNormalization, Dropout
+from tensorflow.keras.models import Sequential
+
+# =====================
+# MODEL ARCHITECTURE
+# =====================
+
+base_model = MobileNetV2(
+    weights=None,
+    include_top=False,
+    input_shape=(224, 224, 3),
+    pooling="avg"
+)
+
+model = Sequential([
+    base_model,
+    BatchNormalization(),
+    Dropout(0.5),
+    Dense(256, activation="relu"),
+    Dropout(0.3),
+    Dense(7, activation="softmax")
+])
+
+# Load trained weights
+model.load_weights("model.weights.h5")
+
+# =====================
+# CLASS NAMES
+# =====================
+
+class_names = [
+    "broken_benches",
+    "fallen_trees",
+    "garbage",
+    "leaky_pipes",
+    "open_manhole",
+    "potholes",
+    "streetlight"
+]
+
+# =====================
+# PREDICTION FUNCTION
+# =====================
+
+def predict_image(image):
+    if image is None:
+        return "No image uploaded", "0%"
+
+    image = image.convert("RGB")
+    image = image.resize((224, 224))
+
+    img = np.array(image, dtype=np.float32) / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    prediction = model.predict(img, verbose=0)
+
+    predicted_class = class_names[np.argmax(prediction)]
+    confidence = float(np.max(prediction))
+
+    return predicted_class, f"{confidence:.2%}"
+
+# =====================
+# UI
+# =====================
+
+description = """
+# 🏙️ Community Issue Classification System
+
+This AI-powered system automatically identifies common civic infrastructure issues from images.
+
+### Detectable Categories
+- 🪑 Broken Benches
+- 🌳 Fallen Trees
+- 🗑️ Garbage
+- 🚰 Leaky Pipes
+- ⚠️ Open Manholes
+- 🕳️ Potholes
+- 💡 Streetlight Issues
+
+### Model Information
+- Model: MobileNetV2 Fine-Tuned Classifier
+- Classes: 7
+- Input Size: 224 × 224 RGB Images
+- Developer: Pauras More
+
+Upload an image below to classify a civic issue.
+"""
+
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=[
+        gr.Textbox(label="Predicted Class"),
+        gr.Textbox(label="Confidence")
+    ],
+    title="Community Issue Classifier",
+    description=description,
+    flagging_mode="never"
+)
+
+demo.launch()

model.weights.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6c84773b74ba6a1d9235e217e11a21a76e5c59339051aff335ffbb28db40d32
+size 10806808

requirements.txt

@@ -0,0 +1,5 @@
+tensorflow==2.20.0
+gradio==6.16.0
+numpy
+pillow
+h5py